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May 24, 2020 22:02
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import math | |
import torch | |
import torch.nn as nn |
ok i Got!~~~Done
i_t = torch.sigmoid(self.U_ix_t + self.ub_i + self.V_ih_t + self.vb_i)
f_t = torch.sigmoid(self.U_fx_t + self.ub_i + self.V_fh_t + self.vb_f)
g_t = torch.tanh( self.U_cx_t + self.ub_i + self.V_ch_t + self.vb_c)
o_t = torch.sigmoid(self.U_ox_t + self.ub_i + self.V_oh_t + self.vb_o)
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import math
import torch
import torch.nn as nn
from torch.autograd import Variable
https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html
https://towardsdatascience.com/building-a-lstm-by-hand-on-pytorch-59c02a4ec091
https://zhuanlan.zhihu.com/p/144132609
class CustomLSTM(nn.Module):
def init(self, i_size: int, h_size: int):
super().init()
self.input_size = i_size
self.hidden_size = h_size
def example_1():
testModel = CustomLSTM(1,1)
input = Variable(torch.FloatTensor([1.0, 1.0, 1.0]), requires_grad=True).view(-1, 1, 1)
output = testModel(input)
print(output, '\n')
##########################################################################################
##########################################################################################
##########################################################################################
class StandardLSTM(nn.Module):
def init(self, i_size: int, h_size: int):
super().init()
self.input_size = i_size
self.hidden_size = h_size
def example_2():
testModel = StandardLSTM(1,1)
input = Variable(torch.FloatTensor([1.0, 1.0, 1.0]), requires_grad=True).view(-1, 1, 1)
output = testModel(input)
print(output, '\n')
if name == "main":
example_1()
example_2()
///////////////////////////////////
(tensor([[[0.8701]],
(tensor([[[0.7038]],
why result diffrent plz help